Date:	Tue, February 5, 2013
Time:	16:00
Place:	Jacobs Campus, IRC building, Seminar room, 3rd floor

Abstract: We extend the theory of operator sampling and identification to apply to operators with stochastic spreading functions. We prove that identification with a delta train signal is possible for a large class of stochastic operators that have the property that the autocorrelation of the spreading function is supported on a set of 4D volume less than one and this support set does not have a defective structure. As an example, we take a common application of stochastic operator identification, radar detection. Under the assumption of wide sense stationarity with uncorrelated scattering (WSSUS), the second order statistics of the spreading function are characterized by the scattering function. We show that our method allows to identify a compactly supported scattering function from the full statistics of a received echo with no regard to the area of the support. On the other hand, analogous to the deterministic case, we show that the restriction of the 4D volume of a support set of the spreading function to be less or equal to one remains necessary for identifiability of a stochastic operator class.